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Emerging Paradigms in Machine Learning

E-BookPDF1 - PDF WatermarkE-Book
498 Seiten
Englisch
Springer Berlin Heidelbergerschienen am31.07.20122013
This  book presents fundamental topics and algorithms that form the core of machine learning (ML) research, as well as emerging paradigms in intelligent system design. The  multidisciplinary nature of machine learning makes it a very fascinating and popular area for research.  The book is aiming at students, practitioners and researchers and captures the diversity and richness of the field of machine learning and intelligent systems.  Several chapters are devoted to computational learning models such as granular computing, rough sets and fuzzy sets An account of applications of well-known learning methods in biometrics, computational stylistics, multi-agent systems, spam classification including an extremely well-written survey on Bayesian networks shed light on the strengths and weaknesses of the methods. Practical studies yielding insight into challenging problems such as learning from incomplete and imbalanced data, pattern recognition of stochastic episodic events and on-line mining of non-stationary data streams are a key part of this book.mehr
Verfügbare Formate
BuchGebunden
EUR106,99
E-BookPDF1 - PDF WatermarkE-Book
EUR96,29

Produkt

KlappentextThis  book presents fundamental topics and algorithms that form the core of machine learning (ML) research, as well as emerging paradigms in intelligent system design. The  multidisciplinary nature of machine learning makes it a very fascinating and popular area for research.  The book is aiming at students, practitioners and researchers and captures the diversity and richness of the field of machine learning and intelligent systems.  Several chapters are devoted to computational learning models such as granular computing, rough sets and fuzzy sets An account of applications of well-known learning methods in biometrics, computational stylistics, multi-agent systems, spam classification including an extremely well-written survey on Bayesian networks shed light on the strengths and weaknesses of the methods. Practical studies yielding insight into challenging problems such as learning from incomplete and imbalanced data, pattern recognition of stochastic episodic events and on-line mining of non-stationary data streams are a key part of this book.
Details
Weitere ISBN/GTIN9783642286995
ProduktartE-Book
EinbandartE-Book
FormatPDF
Format Hinweis1 - PDF Watermark
FormatE107
Erscheinungsjahr2012
Erscheinungsdatum31.07.2012
Auflage2013
ReiheSmart Innovation, Systems and Technologies
Reihen-Nr.13
Seiten498 Seiten
SpracheEnglisch
IllustrationenXXII, 498 p.
Artikel-Nr.1716758
Rubriken
Genre9200

Inhalt/Kritik

Inhaltsverzeichnis
From the content: Emerging Paradigms in Machine Learning: An Introduction.- Extensions of Dynamic Programming as a New Tool for Decision Tree Optimization.- Optimised information abstraction in granular Min/Max clustering.- Mining Incomplete Data-A Rough Set Approach.- Roles Played by Bayesian Networks in Machine Learning: An Empirical Investigation.mehr

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